import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import plotly.express as px

pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 5000)

def dame_variables_categoricas(dataset=None):
    '''
    ----------------------------------------------------------------------------------------------------------
    Función dame_variables_categoricas:
    ----------------------------------------------------------------------------------------------------------
        -Descripción: Función que recibe un dataset y devuelve una lista con los nombres de las 
        variables categóricas
        -Inputs: 
            -- dataset: Pandas dataframe que contiene los datos
        -Return:
            -- lista_variables_categoricas: lista con los nombres de las variables categóricas del
            dataset de entrada con menos de 100 valores diferentes
            -- 1: la ejecución es incorrecta
    '''
    if dataset is None:
        print(u'\nFaltan argumentos por pasar a la función')
        return 1
    lista_variables_categoricas = []
    other = []
    for i in dataset.columns:
        if (dataset[i].dtype!=float) & (dataset[i].dtype!=int):
            unicos = int(len(np.unique(dataset[i].dropna(axis=0, how='all'))))
            if unicos < 100:
                lista_variables_categoricas.append(i)
            else:
                other.append(i)

    return lista_variables_categoricas, other

#Leemos los datos
Base_df = pd.read_csv('../data/Base.csv')
Base_df

print(Base_df.shape, Base_df.drop_duplicates().shape)

(1000000, 32) (1000000, 32)

Base_df.dtypes.to_dict()

{'fraud_bool': dtype('int64'),
 'income': dtype('float64'),
 'name_email_similarity': dtype('float64'),
 'prev_address_months_count': dtype('int64'),
 'current_address_months_count': dtype('int64'),
 'customer_age': dtype('int64'),
 'days_since_request': dtype('float64'),
 'intended_balcon_amount': dtype('float64'),
 'payment_type': dtype('O'),
 'zip_count_4w': dtype('int64'),
 'velocity_6h': dtype('float64'),
 'velocity_24h': dtype('float64'),
 'velocity_4w': dtype('float64'),
 'bank_branch_count_8w': dtype('int64'),
 'date_of_birth_distinct_emails_4w': dtype('int64'),
 'employment_status': dtype('O'),
 'credit_risk_score': dtype('int64'),
 'email_is_free': dtype('int64'),
 'housing_status': dtype('O'),
 'phone_home_valid': dtype('int64'),
 'phone_mobile_valid': dtype('int64'),
 'bank_months_count': dtype('int64'),
 'has_other_cards': dtype('int64'),
 'proposed_credit_limit': dtype('float64'),
 'foreign_request': dtype('int64'),
 'source': dtype('O'),
 'session_length_in_minutes': dtype('float64'),
 'device_os': dtype('O'),
 'keep_alive_session': dtype('int64'),
 'device_distinct_emails_8w': dtype('int64'),
 'device_fraud_count': dtype('int64'),
 'month': dtype('int64')}

Base_df_plot_fraud_bool_status = Base_df['fraud_bool']\
        .value_counts(normalize=True)\
        .mul(100).rename('percent').reset_index()

Base_df_plot_fraud_bool_status_conteo = Base_df['fraud_bool'].value_counts().reset_index()
Base_df_plot_fraud_bool_status_pc = pd.merge(Base_df_plot_fraud_bool_status, Base_df_plot_fraud_bool_status_conteo, on=['fraud_bool'], how='inner')
Base_df_plot_fraud_bool_status_pc

fig = px.histogram(Base_df_plot_fraud_bool_status_pc, x="fraud_bool", y=['percent'])
fig.show()

base_series_null_columns = Base_df.isnull().sum().sort_values(ascending=False)
base_series_null_rows = Base_df.isnull().sum(axis=1).sort_values(ascending=False)
print(base_series_null_columns.shape, base_series_null_rows.shape)

base_null_columnas = pd.DataFrame(base_series_null_columns, columns=['nulos_columnas'])     
base_null_filas = pd.DataFrame(base_series_null_rows, columns=['nulos_filas'])  
base_null_filas['target'] = Base_df['fraud_bool'].copy()
base_null_columnas['porcentaje_columnas'] = base_null_columnas['nulos_columnas']/Base_df.shape[0]
base_null_filas['porcentaje_filas']= base_null_filas['nulos_filas']/Base_df.shape[1]

(32,) (1000000,)

filtered_data = Base_df.loc[:]
# Reemplazamos valores negativos (-1) por NaN en las columnas específicas donde hay nulos
columns_to_replace = ['prev_address_months_count', 'current_address_months_count', 'bank_months_count', 'session_length_in_minutes', 'intended_balcon_amount']
filtered_data[columns_to_replace] = filtered_data[columns_to_replace].applymap(lambda x: np.nan if x < 0 else x)
null_values = filtered_data.isnull().sum()
null_percentage = (null_values / len(Base_df)) * 100
base_null_columnas = pd.DataFrame({'nulos_columnas': null_values, 'porcentaje_columnas': null_percentage})
print(base_null_columnas)

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3456504727.py:4: FutureWarning:

DataFrame.applymap has been deprecated. Use DataFrame.map instead.

                                  nulos_columnas  porcentaje_columnas
fraud_bool                                     0               0.0000
income                                         0               0.0000
name_email_similarity                          0               0.0000
prev_address_months_count                 712920              71.2920
current_address_months_count                4254               0.4254
customer_age                                   0               0.0000
days_since_request                             0               0.0000
intended_balcon_amount                    742523              74.2523
payment_type                                   0               0.0000
zip_count_4w                                   0               0.0000
velocity_6h                                    0               0.0000
velocity_24h                                   0               0.0000
velocity_4w                                    0               0.0000
bank_branch_count_8w                           0               0.0000
date_of_birth_distinct_emails_4w               0               0.0000
employment_status                              0               0.0000
credit_risk_score                              0               0.0000
email_is_free                                  0               0.0000
housing_status                                 0               0.0000
phone_home_valid                               0               0.0000
phone_mobile_valid                             0               0.0000
bank_months_count                         253635              25.3635
has_other_cards                                0               0.0000
proposed_credit_limit                          0               0.0000
foreign_request                                0               0.0000
source                                         0               0.0000
session_length_in_minutes                   2015               0.2015
device_os                                      0               0.0000
keep_alive_session                             0               0.0000
device_distinct_emails_8w                      0               0.0000
device_fraud_count                             0               0.0000
month                                          0               0.0000

base_null_columnas

threshold=0.9
list_vars_not_null = list(base_null_columnas[base_null_columnas['porcentaje_columnas']<threshold].index)
Base_df_filter_null = Base_df.loc[:, list_vars_not_null]
Base_df_filter_null.shape

(1000000, 29)

threshold = 0.9
list_vars_null = list(base_null_columnas[base_null_columnas['porcentaje_columnas'] >= threshold].index)
print("Variables con más del 90% de valores nulos:")
print(list_vars_null)

Variables con más del 90% de valores nulos:
['prev_address_months_count', 'intended_balcon_amount', 'bank_months_count']

base_null_filas

list_cat_vars, other = dame_variables_categoricas(dataset = Base_df_filter_null)
Base_df_filter_null[list_cat_vars] = Base_df_filter_null[list_cat_vars].astype("category")
Base_df_filter_null[list_cat_vars].head()

list_cat_vars

['payment_type', 'employment_status', 'housing_status', 'source', 'device_os']

Base_df_filter_null[list_cat_vars].dtypes

payment_type         category
employment_status    category
housing_status       category
source               category
device_os            category
dtype: object

Base_df_filter_null['fraud_bool'].value_counts()

fraud_bool
0    988971
1     11029
Name: count, dtype: int64

print(Base_df_filter_null["fraud_bool"].value_counts().count())
Base_df_filter_null["fraud_bool"]\
    .apply(lambda x: str(x).lower().strip()).value_counts(normalize=True)

2

fraud_bool
0    0.988971
1    0.011029
Name: proportion, dtype: float64

print(Base_df_filter_null["employment_status"].value_counts().count())
Base_df_filter_null["employment_status"]\
    .apply(lambda x: str(x).lower().strip()).value_counts(normalize=True)#.count()

7

employment_status
ca    0.730252
cb    0.138288
cf    0.044034
cc    0.037758
cd    0.026522
ce    0.022693
cg    0.000453
Name: proportion, dtype: float64

print(Base_df_filter_null["payment_type"].value_counts().count())
Base_df_filter_null["payment_type"]\
    .apply(lambda x: str(x).lower().strip()).value_counts(normalize=True)#.count()

5

payment_type
ab    0.370554
aa    0.258249
ac    0.252071
ad    0.118837
ae    0.000289
Name: proportion, dtype: float64

import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import plotly.express as px
from sklearn.impute import KNNImputer
import scipy.stats as ss
import warnings

pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 5000)

def plot_feature(df, col_name, isContinuous, target):
    """
    Visualize a variable with and without faceting on the loan status.
    - df dataframe
    - col_name is the variable name in the dataframe
    - full_name is the full variable name
    - continuous is True if the variable is continuous, False otherwise
    """
    f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,3), dpi=90)
    
    count_null = df[col_name].isnull().sum()
    if isContinuous:
        
        sns.histplot(df.loc[df[col_name].notnull(), col_name], kde=False, ax=ax1)
    else:
        sns.countplot(df, x=col_name, color='#5975A4', saturation=1, ax=ax1)
    ax1.set_xlabel(col_name)
    ax1.set_ylabel('Count')
    ax1.set_title(col_name+ ' Numero de nulos: '+str(count_null))
    plt.xticks(rotation = 90)


    if isContinuous:
        sns.boxplot(x=col_name, y=target, data=df, ax=ax2)
        ax2.set_ylabel('')
        ax2.set_title(col_name + ' by '+target)
    else:
        data = df.groupby(col_name)[target].value_counts(normalize=True).to_frame('proportion').reset_index() 
        data.columns = [i, target, 'proportion']
        #sns.barplot(x = col_name, y = 'proportion', hue= target, data = data, saturation=1, ax=ax2)
        sns.barplot(x = col_name, y = 'proportion', hue= target, data = data, saturation=1, ax=ax2)
        ax2.set_ylabel(target+' fraction')
        ax2.set_title(target)
        plt.xticks(rotation = 90)
    ax2.set_xlabel(col_name)
    
    plt.tight_layout()

def dame_variables_categoricas(Base_df=None):
    '''
    ----------------------------------------------------------------------------------------------------------
    Función dame_variables_categoricas:
    ----------------------------------------------------------------------------------------------------------
        -Descripción: Función que recibe un dataset y devuelve una lista con los nombres de las 
        variables categóricas
        -Inputs: 
            -- dataset: Pandas dataframe que contiene los datos
        -Return:
            -- lista_variables_categoricas: lista con los nombres de las variables categóricas del
            dataset de entrada con menos de 100 valores diferentes
            -- 1: la ejecución es incorrecta
    '''
    if Base_df is None:
        print(u'\nFaltan argumentos por pasar a la función')
        return 1
    lista_variables_categoricas = []
    other = []
    for i in dataset.columns:
        if (dataset[i].dtype!=float) & (dataset[i].dtype!=int):
            unicos = int(len(np.unique(dataset[i].dropna(axis=0, how='all'))))
            if unicos < 100:
                lista_variables_categoricas.append(i)
            else:
                other.append(i)

    return lista_variables_categoricas, other

def get_corr_matrix(dataset = None, metodo='pearson', size_figure=[10,8]):
    # Para obtener la correlación de Spearman, sólo cambiar el metodo por 'spearman'

    if dataset is None:
        print(u'\nHace falta pasar argumentos a la función')
        return 1
    sns.set(style="white")
    # Compute the correlation matrix
    corr = dataset.corr(method=metodo) 
    # Set self-correlation to zero to avoid distraction
    for i in range(corr.shape[0]):
        corr.iloc[i, i] = 0
    # Set up the matplotlib figure
    f, ax = plt.subplots(figsize=size_figure)
    # Draw the heatmap with the mask and correct aspect ratio
    sns.heatmap(corr, center=0,
                square=True, linewidths=.5,  cmap ='viridis' ) #cbar_kws={"shrink": .5}
    plt.show()
    
    return 0

def get_deviation_of_mean_perc(Base_df, list_var_continuous, target, multiplier):
    """
    Devuelve el porcentaje de valores que exceden del intervalo de confianza
    :type series:
    :param multiplier:
    :return:
    """
    pd_final = pd.DataFrame()
    
    for i in list_var_continuous:
        
        series_mean = Base_df[i].mean()
        series_std = Base_df[i].std()
        std_amp = multiplier * series_std
        left = series_mean - std_amp
        right = series_mean + std_amp
        size_s = Base_df[i].size
        
        perc_goods = Base_df[i][(Base_df[i] >= left) & (Base_df[i] <= right)].size/size_s
        perc_excess = Base_df[i][(Base_df[i] < left) | (Base_df[i] > right)].size/size_s
        
        if perc_excess>0:    
            pd_concat_percent = pd.DataFrame(Base_df[target][(Base_df[i] < left) | (Base_df[i] > right)]\
                                            .value_counts(normalize=True).reset_index()).T
            pd_concat_percent.columns = [pd_concat_percent.iloc[0,0], 
                                         pd_concat_percent.iloc[0,1]]
            pd_concat_percent = pd_concat_percent.drop('fraud_bool',axis=0)
            pd_concat_percent['variable'] = i
            pd_concat_percent['sum_outlier_values'] = Base_df[i][(Base_df[i] < left) | (Base_df[i] > right)].size
            pd_concat_percent['porcentaje_sum_null_values'] = perc_excess
            pd_final = pd.concat([pd_final, pd_concat_percent], axis=0).reset_index(drop=True)
            
    if pd_final.empty:
        print('No existen variables con valores nulos')
        
    return pd_final

def get_percent_null_values_target(Base_df, list_var_continuous, target):

    base_df_final = pd.DataFrame()
    for i in list_var_continuous:
        if Base_df[i].isnull().sum()>0:
            base_concat_percent = pd.DataFrame(Base_df[target][Base_df[i].isnull()]\
                                            .value_counts(normalize=True).reset_index()).T
            base_concat_percent.columns = [base_concat_percent.iloc[0,0], 
                                         base_concat_percent.iloc[0,1]]
            base_concat_percent = base_concat_percent.drop('index',axis=0)
            base_concat_percent['variable'] = i
            base_concat_percent['sum_null_values'] = Base_df[i].isnull().sum()
            base_concat_percent['porcentaje_sum_null_values'] = Base_df[i].isnull().sum()/Base_df.shape[0]
            base_final = pd.concat([base_df_final, base_concat_percent], axis=0).reset_index(drop=True)
            
    if base_df_final.empty:
        print('No existen variables con valores nulos')
        
    return base_df_final

def cramers_v(confusion_matrix):
    """ 
    calculate Cramers V statistic for categorial-categorial association.
    uses correction from Bergsma and Wicher,
    Journal of the Korean Statistical Society 42 (2013): 323-328
    
    confusion_matrix: tabla creada con pd.crosstab()
    
    """
    chi2 = ss.chi2_contingency(confusion_matrix)[0]
    n = confusion_matrix.sum()
    phi2 = chi2 / n
    r, k = confusion_matrix.shape
    phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))
    rcorr = r - ((r-1)**2)/(n-1)
    kcorr = k - ((k-1)**2)/(n-1)
    return np.sqrt(phi2corr / min((kcorr-1), (rcorr-1)))

Base_df.columns

Index(['fraud_bool', 'income', 'name_email_similarity',
       'prev_address_months_count', 'current_address_months_count',
       'customer_age', 'days_since_request', 'intended_balcon_amount',
       'payment_type', 'zip_count_4w', 'velocity_6h', 'velocity_24h',
       'velocity_4w', 'bank_branch_count_8w',
       'date_of_birth_distinct_emails_4w', 'employment_status',
       'credit_risk_score', 'email_is_free', 'housing_status',
       'phone_home_valid', 'phone_mobile_valid', 'bank_months_count',
       'has_other_cards', 'proposed_credit_limit', 'foreign_request', 'source',
       'session_length_in_minutes', 'device_os', 'keep_alive_session',
       'device_distinct_emails_8w', 'device_fraud_count', 'month'],
      dtype='object')

lista_variables_categoricas = variables_categoricas = ["payment_type","employment_status","housing_status","email_is_free","phone_home_valid",
                                "phone_mobile_valid","has_other_cards","foreign_request","source","device_os","keep_alive_session", "fraud_bool"]

for columna in variables_categoricas:
    Base_df[columna] = Base_df[columna].astype('category')   
lista_variables_categoricas

['payment_type',
 'employment_status',
 'housing_status',
 'email_is_free',
 'phone_home_valid',
 'phone_mobile_valid',
 'has_other_cards',
 'foreign_request',
 'source',
 'device_os',
 'keep_alive_session',
 'fraud_bool']

Base_df['prev_address_months_count'] = Base_df['prev_address_months_count'].astype(float)
Base_df['current_address_months_count'] = Base_df['current_address_months_count'].astype(float)
Base_df['zip_count_4w'] = Base_df['zip_count_4w'].astype(float)
Base_df['bank_branch_count_8w'] = Base_df['bank_branch_count_8w'].astype(float)
Base_df['credit_risk_score'] = Base_df['credit_risk_score'].astype(float)

Base_df.dtypes

fraud_bool                            int64
income                              Float64
name_email_similarity               Float64
prev_address_months_count           float64
current_address_months_count        float64
customer_age                          int64
days_since_request                  Float64
intended_balcon_amount              Float64
payment_type                        float64
zip_count_4w                        float64
velocity_6h                         Float64
velocity_24h                        Float64
velocity_4w                         Float64
bank_branch_count_8w                float64
date_of_birth_distinct_emails_4w      int64
employment_status                   float64
credit_risk_score                   float64
email_is_free                         int64
housing_status                      float64
phone_home_valid                      int64
phone_mobile_valid                    int64
bank_months_count                     int64
has_other_cards                       int64
proposed_credit_limit               Float64
foreign_request                       int64
source                              float64
session_length_in_minutes           Float64
device_os                           float64
keep_alive_session                    int64
device_distinct_emails_8w             int64
device_fraud_count                    int64
month                                 int64
dtype: object

import plotly.express as px

Base_df_plot_fraud_bool_status = Base_df['fraud_bool']\
        .value_counts(normalize=True)\
        .mul(100).rename('percent').reset_index()

Base_df_plot_fraud_bool_status_conteo = Base_df['fraud_bool'].value_counts().reset_index()
Base_df_plot_fraud_bool_status_pc = pd.merge(Base_df_plot_fraud_bool_status, 
                                  Base_df_plot_fraud_bool_status_conteo, on=['fraud_bool'], how='inner')

fig = px.histogram(Base_df_plot_fraud_bool_status_pc, x="fraud_bool", y=['percent'])
fig.show()

from sklearn.model_selection import train_test_split
X_base_df, X_base_df_test, y_base_df, y_base_df_test = train_test_split(Base_df.drop('fraud_bool',axis=1), 
                                                                     Base_df['fraud_bool'], 
                                                                     stratify=Base_df['fraud_bool'], 
                                                                     test_size=0.2)
Base_df_train = pd.concat([X_base_df, y_base_df],axis=1)
Base_df_test = pd.concat([X_base_df_test, y_base_df_test],axis=1)

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning:

is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning:

is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.

print('== Train\n', Base_df_train['fraud_bool'].value_counts(normalize=True))
print('== Test\n', Base_df_test['fraud_bool'].value_counts(normalize=True))

== Train
 fraud_bool
0    0.988971
1    0.011029
Name: proportion, dtype: float64
== Test
 fraud_bool
0    0.98897
1    0.01103
Name: proportion, dtype: float64

#Vemos los nombres de las columnas que componen el dataframe
Base_df.columns

Index(['fraud_bool', 'income', 'name_email_similarity',
       'prev_address_months_count', 'current_address_months_count',
       'customer_age', 'days_since_request', 'intended_balcon_amount',
       'payment_type', 'zip_count_4w', 'velocity_6h', 'velocity_24h',
       'velocity_4w', 'bank_branch_count_8w',
       'date_of_birth_distinct_emails_4w', 'employment_status',
       'credit_risk_score', 'email_is_free', 'housing_status',
       'phone_home_valid', 'phone_mobile_valid', 'bank_months_count',
       'has_other_cards', 'proposed_credit_limit', 'foreign_request', 'source',
       'session_length_in_minutes', 'device_os', 'keep_alive_session',
       'device_distinct_emails_8w', 'device_fraud_count', 'month'],
      dtype='object')

base_null_columnas

lista_variables_numericas = ["income", "name_email_similarity", "prev_address_months_count", "current_address_months_count", "customer_age", "days_since_request",
                            "intended_balcon_amount", "zip_count_4w", "velocity_6h", "velocity_24h", "velocity_4w", "bank_branch_count_8w", "date_of_birth_distinct_emails_4w",
                            "credit_risk_score", "bank_months_count", "proposed_credit_limit", "session_length_in_minutes", "device_distinct_emails_8w", "device_fraud_count",
                            "month"]
target = 'fraud_bool'
Base_df_train[target] = Base_df_train[target].astype(str)

%%time
for i in list(Base_df_train.columns):
    if (Base_df_train[i].dtype==float) & (i!='fraud_bool'):
        plot_feature(Base_df_train, col_name=i, isContinuous=True, target='fraud_bool')
    elif  i!='fraud_bool':
        plot_feature(Base_df_train, col_name=i, isContinuous=False, target='fraud_bool')

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:9: RuntimeWarning:

More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/var/folders/1l/q9rgmm2d30j9k7npzfz8srnr0000gn/T/ipykernel_17354/3531263144.py:28: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/categorical.py:641: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

/Users/ruthjorganestorres/anaconda3/envs/Practica_1/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning:

is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead

CPU times: user 3min 57s, sys: 10.3 s, total: 4min 8s
Wall time: 4min 38s

lista_variables_numericas

['income',
 'name_email_similarity',
 'prev_address_months_count',
 'current_address_months_count',
 'customer_age',
 'days_since_request',
 'intended_balcon_amount',
 'zip_count_4w',
 'velocity_6h',
 'velocity_24h',
 'velocity_4w',
 'bank_branch_count_8w',
 'date_of_birth_distinct_emails_4w',
 'credit_risk_score',
 'bank_months_count',
 'proposed_credit_limit',
 'session_length_in_minutes',
 'device_distinct_emails_8w',
 'device_fraud_count',
 'month']

Base_df_train

get_deviation_of_mean_perc(Base_df_train, lista_variables_numericas, target='fraud_bool', multiplier=3)

corr_matrix =get_corr_matrix(dataset = Base_df_train[lista_variables_numericas], 
                metodo='pearson', size_figure=[10,8])

corr = Base_df[lista_variables_numericas].corr('pearson')
new_corr = corr.abs()
new_corr.loc[:,:] = np.tril(new_corr, k=-1) # below main lower triangle of an array
new_corr = new_corr.stack().to_frame('correlation').reset_index().sort_values(by='correlation', ascending=False)
new_corr[new_corr['correlation']>0.5]

get_percent_null_values_target(Base_df, lista_variables_numericas, target='fraud_bool')

No existen variables con valores nulos

variables_categoricas

['payment_type',
 'employment_status',
 'housing_status',
 'email_is_free',
 'phone_home_valid',
 'phone_mobile_valid',
 'has_other_cards',
 'foreign_request',
 'source',
 'device_os',
 'keep_alive_session',
 'fraud_bool']

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["payment_type"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

payment_type      AA      AB      AC     AD   AE
fraud_bool                                      
0             205602  293088  198341  93927  219
1               1091    3340    3350   1041    1

0.038745961511433426

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["employment_status"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

employment_status      CA      CB     CC     CD     CE     CF   CG
fraud_bool                                                        
0                  577124  109852  29596  20969  18110  35164  362
1                    7100     775    743     81     47     72    5

0.038875527423075075

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["housing_status"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

housing_status      BA      BB      BC     BD      BE    BF   BG
fraud_bool                                                      
0               130685  207594  295947  20764  134644  1338  205
1                 5046    1282    1856    173     460     5    1

0.11363557295742692

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["email_is_free"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

email_is_free       0       1
fraud_bool                   
0              373037  418140
1                2992    5831

0.02766654939510862

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["phone_home_valid"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

phone_home_valid       0       1
fraud_bool                      
0                 459847  331330
1                   6564    2259

0.034441578918114994

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["phone_mobile_valid"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

phone_mobile_valid      0       1
fraud_bool                       
0                   86972  704205
1                    1339    7484

0.013878204789521916

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["has_other_cards"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

has_other_cards       0       1
fraud_bool                     
0                613852  177325
1                  8061     762

0.0345532374987317

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["foreign_request"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

foreign_request       0      1
fraud_bool                    
0                771461  19716
1                  8386    437

0.01632489159553951

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["source"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

source      INTERNET  TELEAPP
fraud_bool                   
0             785652     5525
1               8729       94

0.004378247355559008

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["device_os"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

device_os    linux  macintosh   other  windows   x11
fraud_bool                                          
0           264513      42470  272832   205628  5734
1             1385        605    1581     5189    63

0.08002228306972355

confusion_matrix = pd.crosstab(Base_df["fraud_bool"], Base_df_train["keep_alive_session"])
print(confusion_matrix)
cramers_v(confusion_matrix.values)

keep_alive_session       0       1
fraud_bool                        
0                   332722  458455
1                     5807    3016

0.05020603874422213

Base_df_train.to_csv("../data/train_base_data_preprocessing.csv")
Base_df_test.to_csv("../data/test_base_data_preprocessing.csv")

print(Base_df_train.shape) 
print(Base_df_test.shape)

(800000, 32)
(200000, 32)

import category_encoders as ce
from sklearn.preprocessing import OneHotEncoder
from sklearn.ensemble import GradientBoostingClassifier
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.preprocessing import StandardScaler

Base_df_train = pd.read_csv("../data/train_base_data_preprocessing.csv")\
                .set_index(['month'])
Base_df_test = pd.read_csv("../data/test_base_data_preprocessing.csv")\
                .set_index(['month'])

Base_df_train.columns

Index(['Unnamed: 0', 'income', 'name_email_similarity',
       'prev_address_months_count', 'current_address_months_count',
       'customer_age', 'days_since_request', 'intended_balcon_amount',
       'payment_type', 'zip_count_4w', 'velocity_6h', 'velocity_24h',
       'velocity_4w', 'bank_branch_count_8w',
       'date_of_birth_distinct_emails_4w', 'employment_status',
       'credit_risk_score', 'email_is_free', 'housing_status',
       'phone_home_valid', 'phone_mobile_valid', 'bank_months_count',
       'has_other_cards', 'proposed_credit_limit', 'foreign_request', 'source',
       'session_length_in_minutes', 'device_os', 'keep_alive_session',
       'device_distinct_emails_8w', 'device_fraud_count', 'fraud_bool'],
      dtype='object')

Base_df.dtypes

fraud_bool                          category
income                               float64
name_email_similarity                float64
prev_address_months_count            float64
current_address_months_count         float64
customer_age                           int64
days_since_request                   float64
intended_balcon_amount               float64
payment_type                        category
zip_count_4w                         float64
velocity_6h                          float64
velocity_24h                         float64
velocity_4w                          float64
bank_branch_count_8w                 float64
date_of_birth_distinct_emails_4w       int64
employment_status                   category
credit_risk_score                    float64
email_is_free                       category
housing_status                      category
phone_home_valid                    category
phone_mobile_valid                  category
bank_months_count                      int64
has_other_cards                     category
proposed_credit_limit                float64
foreign_request                     category
source                              category
session_length_in_minutes            float64
device_os                           category
keep_alive_session                  category
device_distinct_emails_8w              int64
device_fraud_count                     int64
month                                  int64
dtype: object

	fraud_bool	income	name_email_similarity	prev_address_months_count	current_address_months_count	customer_age	days_since_request	intended_balcon_amount	payment_type	zip_count_4w	velocity_6h	velocity_24h	velocity_4w	bank_branch_count_8w	date_of_birth_distinct_emails_4w	employment_status	credit_risk_score	email_is_free	housing_status	phone_home_valid	phone_mobile_valid	bank_months_count	has_other_cards	proposed_credit_limit	foreign_request	source	session_length_in_minutes	device_os	keep_alive_session	device_distinct_emails_8w	device_fraud_count	month
0	1	0.9	0.166828	-1	88	50	0.020925	-1.331345	AA	769	10650.765523	3134.319630	3863.647740	1	6	CA	185	0	BA	1	0	24	0	500.0	0	INTERNET	3.888115	windows	0	1	0	7
1	1	0.9	0.296286	-1	144	50	0.005418	-0.816224	AB	366	534.047319	2670.918292	3124.298166	718	3	CA	259	1	BA	0	0	15	0	1500.0	0	INTERNET	31.798819	windows	0	1	0	7
2	1	0.9	0.044985	-1	132	40	3.108549	-0.755728	AC	870	4048.534263	2893.621498	3159.590679	1	14	CB	177	1	BA	0	1	-1	0	200.0	0	INTERNET	4.728705	other	0	1	0	7
3	1	0.9	0.159511	-1	22	50	0.019079	-1.205124	AB	810	3457.064063	4054.908412	3022.261812	1921	6	CA	110	1	BA	0	1	31	1	200.0	0	INTERNET	2.047904	linux	0	1	0	7
4	1	0.9	0.596414	-1	218	50	0.004441	-0.773276	AB	890	5020.341679	2728.237159	3087.670952	1990	2	CA	295	1	BA	1	0	31	0	1500.0	0	INTERNET	3.775225	macintosh	1	1	0	7
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
999995	0	0.6	0.192631	-1	104	40	0.030592	-1.044454	AB	804	7905.711839	8341.468557	4972.635997	1	8	CA	75	1	BC	1	1	25	0	200.0	0	INTERNET	8.511502	linux	1	1	0	4
999996	0	0.8	0.322989	148	9	50	1.628119	-1.409803	AC	3306	5391.470463	4955.170808	5022.728108	0	2	CC	154	1	BC	1	1	-1	0	200.0	0	INTERNET	8.967865	windows	0	1	0	4
999997	0	0.8	0.879403	-1	30	20	0.018563	34.692760	AA	1522	8063.102636	5670.654316	4377.196321	2023	6	CF	64	0	BC	0	1	11	0	200.0	0	INTERNET	8.195531	other	0	1	0	4
999998	0	0.9	0.762112	-1	189	20	0.015352	94.661055	AA	1418	8092.641762	3982.582204	4394.803296	1678	6	CA	163	0	BA	1	0	28	0	500.0	0	INTERNET	4.336064	windows	1	1	0	4
999999	0	0.2	0.697452	-1	321	20	2.655916	9.908499	AA	951	6169.630036	3695.308261	4352.334543	2	12	CA	36	1	BE	0	1	15	0	200.0	0	INTERNET	6.717022	linux	0	1	0	4

	income	name_email_similarity	prev_address_months_count	current_address_months_count	customer_age	days_since_request	intended_balcon_amount	payment_type	zip_count_4w	velocity_6h	velocity_24h	velocity_4w	bank_branch_count_8w	date_of_birth_distinct_emails_4w	employment_status	credit_risk_score	email_is_free	housing_status	phone_home_valid	phone_mobile_valid	bank_months_count	has_other_cards	proposed_credit_limit	foreign_request	source	session_length_in_minutes	device_os	keep_alive_session	device_distinct_emails_8w	device_fraud_count	month	fraud_bool
589952	0.6	0.429674	-1.0	32.0	30	0.010047	15.299031	AA	1635.0	8736.670001	5399.266061	4348.347034	8.0	13	CA	134.0	0	BE	0	1	20	1	200.0	0	INTERNET	2.276646	other	1	1	0	6	0
326603	0.9	0.891825	-1.0	107.0	40	0.012554	-0.644093	AC	1334.0	5688.940109	8144.084164	5518.511924	0.0	7	CA	63.0	0	BA	1	0	-1	0	200.0	0	INTERNET	9.679169	windows	0	1	0	2	0
149122	0.9	0.598939	-1.0	174.0	20	0.019957	-0.919622	AD	2459.0	5604.175968	4860.885761	4796.591264	1.0	11	CA	244.0	0	BA	0	1	1	0	1000.0	0	INTERNET	2.463254	windows	1	1	0	3	0
142133	0.5	0.531510	-1.0	140.0	60	0.025658	-0.897969	AB	1406.0	7200.434217	5887.623549	4781.552066	2077.0	5	CA	134.0	1	BC	1	0	2	0	500.0	0	INTERNET	24.439424	windows	1	1	0	3	0
133021	0.2	0.083350	59.0	17.0	30	13.750229	-0.675878	AB	591.0	9052.031397	6184.825380	4853.898984	1.0	10	CA	106.0	0	BC	0	1	28	0	200.0	0	INTERNET	5.931679	windows	1	1	0	3	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
783602	0.1	0.172221	-1.0	74.0	30	0.003580	-0.520913	AB	550.0	793.837064	4564.594295	4156.615833	14.0	6	CA	84.0	0	BB	1	1	1	0	200.0	0	INTERNET	8.853195	other	1	1	0	5	0
587705	0.9	0.085711	-1.0	39.0	30	0.009932	-0.688571	AB	885.0	6030.723514	2689.887620	3098.937256	15.0	6	CA	118.0	1	BC	0	1	1	0	200.0	0	INTERNET	11.322296	linux	1	1	0	6	0
816053	0.6	0.084049	-1.0	39.0	30	0.012224	51.120107	AA	1092.0	11609.777812	6419.402777	4154.172059	921.0	14	CA	56.0	1	BE	0	1	2	1	1500.0	0	INTERNET	1.647088	linux	1	1	0	5	0
255857	0.5	0.601015	83.0	23.0	30	0.020667	48.512256	AA	1474.0	7355.383548	5242.112445	5594.437459	1.0	4	CB	201.0	0	BB	0	1	20	0	1000.0	0	INTERNET	9.120114	linux	0	1	0	2	0
596849	0.6	0.908472	-1.0	146.0	40	0.012759	-0.805300	AB	627.0	2178.086557	3270.257629	4138.185940	1367.0	4	CB	159.0	0	BB	0	1	15	0	200.0	0	INTERNET	13.147237	other	1	1	0	6	0

	0	1	variable	sum_outlier_values	porcentaje_sum_null_values
0	0.993783	0.006217	prev_address_months_count	20267	0.025334
1	0.984579	0.015421	current_address_months_count	17184	0.021480
2	0.957102	0.042898	customer_age	6294	0.007867
3	0.98907	0.01093	days_since_request	14273	0.017841
4	0.99011	0.00989	intended_balcon_amount	15268	0.019085
5	0.990713	0.009287	zip_count_4w	13029	0.016286
6	0.993665	0.006335	velocity_6h	3473	0.004341
7	0.995546	0.004454	velocity_24h	449	0.000561
8	0.989667	0.010333	bank_branch_count_8w	32807	0.041009
9	0.993024	0.006976	date_of_birth_distinct_emails_4w	5017	0.006271
10	0.965648	0.034352	credit_risk_score	2911	0.003639
11	0.871784	0.128216	proposed_credit_limit	4898	0.006123
12	0.980701	0.019299	session_length_in_minutes	18913	0.023641
13	0.962992	0.037008	device_distinct_emails_8w	25508	0.031885

VALORES MISSING, OUTLIER Y CORRELACIONES¶

1. Cambiamos el formato de las variables¶

2. Separación train y test¶

3. Análisis de cada variable con gráficos descriptivos¶

Variables numéricas: valores missing, outliers y correlaciones¶

Análisis de correlación de las variables¶

Valores nulos¶

Variables categóricas¶

Coeficiente de Cramer¶

Tratamiento de valores nulos¶

Guardado de la tabla¶

Codificación de las variables categoricas, escalado y modelo¶

	nulos_columnas	porcentaje_columnas
fraud_bool	0	0.0000
income	0	0.0000
name_email_similarity	0	0.0000
prev_address_months_count	712920	71.2920
current_address_months_count	4254	0.4254
customer_age	0	0.0000
days_since_request	0	0.0000
intended_balcon_amount	742523	74.2523
payment_type	0	0.0000
zip_count_4w	0	0.0000
velocity_6h	0	0.0000
velocity_24h	0	0.0000
velocity_4w	0	0.0000
bank_branch_count_8w	0	0.0000
date_of_birth_distinct_emails_4w	0	0.0000
employment_status	0	0.0000
credit_risk_score	0	0.0000
email_is_free	0	0.0000
housing_status	0	0.0000
phone_home_valid	0	0.0000
phone_mobile_valid	0	0.0000
bank_months_count	253635	25.3635
has_other_cards	0	0.0000
proposed_credit_limit	0	0.0000
foreign_request	0	0.0000
source	0	0.0000
session_length_in_minutes	2015	0.2015
device_os	0	0.0000
keep_alive_session	0	0.0000
device_distinct_emails_8w	0	0.0000
device_fraud_count	0	0.0000
month	0	0.0000

	nulos_filas	target	porcentaje_filas
0	0	1	0.0
666657	0	0	0.0
666659	0	0	0.0
666660	0	0	0.0
666661	0	0	0.0
...	...	...	...
333337	0	0	0.0
333338	0	0	0.0
333339	0	0	0.0
333340	0	0	0.0
999999	0	0	0.0

	level_0	level_1	correlation
372	month	velocity_4w	0.848100
313	proposed_credit_limit	credit_risk_score	0.606141
371	month	velocity_24h	0.549919
209	velocity_4w	velocity_24h	0.539115

	fraud_bool	percent	count
0	0	98.8971	988971
1	1	1.1029	11029